Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-24T17:45:54.534Z Has data issue: false hasContentIssue false

A sufficient condition for the existence of an invariant probability measure for Markov processes

Published online by Cambridge University Press:  14 July 2016

O. L. V. Costa*
Affiliation:
Universidade de São Paulo
F. Dufour*
Affiliation:
Université Bordeaux I and Université Bordeaux IV
*
Postal address: Departamento de Engenharia de Telecomunicações e Controle, Escola Politécnica da Universidade de São Paulo, São Paulo, 05508 900, Brazil. Email address: [email protected]
∗∗Postal address: Mathématiques Appliqées de Bordeaux, Université Bordeaux I, 351 cours de la Liberation, 33405 Talence Cedex, France. Email address: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

In this paper, it is shown that the Foster-Lyapunov criterion is sufficient to ensure the existence of an invariant probability measure for both discrete- and continuous-time Markov processes without any additional hypotheses (such as irreducibility).

Type
Short Communications
Copyright
© Applied Probability Trust 2005 

References

Azéma, J., Kaplan-Duflo, M. and Revuz, D. (1967). Mesure invariante sur les classes récurrentes des processus de Markov. Z. Wahrscheinlichkeitsth. 8, 157181.CrossRefGoogle Scholar
Costa, O. L. V. and Dufour, F. (2005). On the ergodic decomposition for a class of Markov chains. Stoch. Process. Appl. 115, 401415.Google Scholar
Down, D., Meyn, S. P. and Tweedie, R. L. (1995). Exponential and uniform ergodicity of Markov processes. Ann. Prob. 23, 16711691.Google Scholar
Lasserre, J. B. (1997). Invariant probabilities for Markov chains on a metric space. Statist. Prob. Lett. 34, 259266.Google Scholar
Meyn, S. P. and Tweedie, R. L. (1992). Stability of Markovian processes. I. Criteria for discrete-time chains. Adv. Appl. Prob. 24, 542574.Google Scholar
Meyn, S. P. and Tweedie, R. L. (1993). Stability of Markovian processes. II. Continuous-time processes and sampled chains. Adv. Appl. Prob. 25, 487517.Google Scholar
Meyn, S. P. and Tweedie, R. L. (1993). Stability of Markovian processes. III. {Foster–Lyapunov} criteria for continuous-time processes. Adv. Appl. Prob. 25, 518548.Google Scholar
Meyn, S. P. and Tweedie, R. L. (1993). {Markov Chains and Stochastic Stability}. Springer, Berlin.Google Scholar
Tweedie, R. L. (1976). Criteria for classifying general Markov chains. Adv. Appl. Prob. 8, 737771.CrossRefGoogle Scholar
Tweedie, R. L. (1988). Invariant measures for {Markov chains with no irreducibility assumptions.} In A Celebration of Applied Probability (J. Appl. Prob. Spec. Vol. 25A), Applied Probability Trust, Sheffield, pp. 275285.Google Scholar
Tweedie, R. L. (2001). Drift conditions and invariant measures for Markov chains. Stoch. Process. Appl. 92, 345354.Google Scholar